A Study of the Mobile Games Recommendation Systems Based-on Text Mining

• Main Authors: Tzu-Jui Sun, 孫慈睿
• Other Authors: 蕭瑞祥
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/4temm3

碩士 === 淡江大學 === 資訊管理學系碩士班 === 104 === Mobile games have already become one of the indispensable part of everyone’s life.Lots of people choose to or not to download the game basing on the comments other gamer gave it on Google Play or App Store . But for the purpose of better overall rank , some game producers use fake comments to cover the actual ones . While the 巴哈姆特 game forum provides a place for gamers to exchange their gaming experiences , not written by experts or game producers . The research selects some of the articles and information on the forum , finds out the articles that contains ratings and giving them points , finally sorts the games as a recommendation. This research uses systems development as a methodology in information systems (is) proposed by Nunamaker(1991) and a few people , aiming to analyzing internet comments and sorting out the comments given by gamers. Finally we calculate the information and list the game rankings according to our calculation , making users understand more about the true ratings of the game , also , the users’ comments can be conveyed to the game producers as standards of improvement . The objects of our study included 3 games: 七騎士,列王的紛爭,神魔之塔 which were all the top 6 games on Google Play revenue ranking list . The research information was collected from the articles on 巴哈姆特, and the articles were mainly in the sub-boards called “all articles” and “easy talking” in the forum. We found out the articles that contained ratings and gave them points ,also , we verified them manually. The accuracy rate were 64.5%, 67.4%, 73.3% . Then we calculated and came out with a game ranking according to their average points . We made survey questionnaires to find out which ranking bore more resemblance to their own ranking. The results showed that 54.1% of the respondents thought our research described better , also , the suitability of the recommending system reached 75% . As a conclusion , the comments and ratings collected from 巴哈姆特 are more corresponding than those collected from Google Play and App Store.